Investigation of Deep Neural Network Compression Based on Tucker Decomposition for the Classification of Lesions in Cavity Oral

Abstract

Cancer in the oral cavity is one of the most common, making it necessary to investigate lesions that could develop into cancer. Initial stage lesions, called dysplasia, can develop into more severe stages of the disease and are characterized by variations in the shape and size of the nucleus of epithelial tissue cells. Due to advances in the areas of digital image processing and artificial intelligence, diagnostic aid systems (CAD) have become a tool to help reduce the difficulties of analyzing and classifying lesions. This paper presents an investigation of the Tucker decomposition in tensors for different CNN models to classify dysplasia in histological images of the oral cavity. In addition to the Tucker decomposition, this study investigates the normalization of H&E dyes on the optimized CNN models to evaluate the behavior of the architectures in the classification stage of dysplasia lesions. The results show that for some of the optimized models, the use of normalization contributed to the performance of the CNNs for classifying dysplasia lesions. However, when the features obtained from the final layers of the CNNs associated with the machine learning algorithms were analyzed, it was noted that the normalization process affected performance during classification.